1 Aggregated and atomic scores per method

#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
#> # A tibble: 1 × 2
#>   late_integration GlobalScore
#>   <chr>                  <dbl>
#> 1 limean                 0.553

2 Early integration_table

#> # A tibble: 2 × 2
#>   early_integration GlobalScore
#>   <chr>                   <dbl>
#> 1 OnlyMet                 0.661
#> 2 OnlyRna                 0.359

3 Visualisations of the different metrics

3.1 Aggregated scores

3.1.1 PP

3.1.2 FS

3.1.3 LI

3.2 MAE

3.2.1 PP

3.2.2 FS

3.2.2.1 MET

boxplots_fsMET_mae = ggplot(results_li, aes(x = feature_selection_mixMET, y = mae, fill = feature_selection_mixMET)) +

geom_boxplot() + facet_wrap(~ dataset, scales = “free_x”) + theme( axis.text.x = element_text(angle = 45, hjust = 1, size = 8), panel.border = element_rect(color = “black”, fill = NA, linewidth = 0.5), plot.title = element_text(hjust = 0.5), legend.position = “bottom” ) + labs( title = “Estimation error (-> 0), per dataset”, x = “Methods”, y = “MAE”, fill = “Feature selection for mixMET” ) plotly::layout( plotly::ggplotly(boxplots_fsMET_mae), # boxmode = “group”, legend = list( # x = 0, # y = -0.3, # xanchor = ‘left’, # yanchor = ‘bottom’, orientation = ‘h’) )

3.2.2.2 RNA

boxplots_fsRNA_mae = ggplot(results_li, aes(x = feature_selection_mixRNA, y = mae, fill = feature_selection_mixRNA)) + geom_boxplot() + facet_wrap(~ dataset, scales = “free_x”) + theme( axis.text.x = element_text(angle = 45, hjust = 1, size = 8), panel.border = element_rect(color = “black”, fill = NA, linewidth = 0.5), plot.title = element_text(hjust = 0.5), legend.position = “bottom” ) + labs( title = “Estimation error (-> 0), per dataset”, x = “Methods”, y = “MAE”, fill = “Feature selection for mixRNA:” ) plotly::layout( plotly::ggplotly(boxplots_fsRNA_mae), # boxmode = “group”, legend = list( # x = 0, # y = -0.3, # xanchor = ‘left’, # yanchor = ‘bottom’, orientation = ‘h’) )


### LI


```{=html}
<div class="plotly html-widget html-fill-item" id="htmlwidget-9e17858662ba23c369c5" style="width:900px;height:900px;"></div>
<script type="application/json" data-for="htmlwidget-9e17858662ba23c369c5">{"x":{"data":[{"x":[1],"y":[0.13292324746349266],"hoverinfo":"y","type":"box","fillcolor":"rgba(248,118,109,1)","marker":{"opacity":null,"outliercolor":"rgba(0,0,0,1)","line":{"width":1.8897637795275593,"color":"rgba(0,0,0,1)"},"size":5.6692913385826778},"line":{"color":"rgba(51,51,51,1)","width":1.8897637795275593},"name":"limean","legendgroup":"limean","showlegend":true,"xaxis":"x","yaxis":"y","frame":null}],"layout":{"margin":{"t":52.529680365296812,"r":7.3059360730593621,"b":43.224191836703355,"l":48.949771689497723},"plot_bgcolor":"rgba(255,255,255,1)","paper_bgcolor":"rgba(255,255,255,1)","font":{"color":"rgba(0,0,0,1)","family":"","size":14.611872146118724},"title":{"text":"Estimation error (-> 0), per dataset","font":{"color":"rgba(0,0,0,1)","family":"","size":17.534246575342465},"x":0.5,"xref":"paper"},"xaxis":{"domain":[0,1],"automargin":true,"type":"linear","autorange":false,"range":[0.40000000000000002,1.6000000000000001],"tickmode":"array","ticktext":["limean"],"tickvals":[1],"categoryorder":"array","categoryarray":["limean"],"nticks":null,"ticks":"outside","tickcolor":"rgba(179,179,179,1)","ticklen":3.6529680365296811,"tickwidth":0.33208800332088001,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":10.62681610626816},"tickangle":-45,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"gridcolor":"rgba(222,222,222,1)","gridwidth":0.33208800332088001,"zeroline":false,"anchor":"y","title":"","hoverformat":".2f"},"annotations":[{"text":"Methods","x":0.5,"y":0,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(0,0,0,1)","family":"","size":14.611872146118724},"xref":"paper","yref":"paper","textangle":-0,"xanchor":"center","yanchor":"top","annotationType":"axis","yshift":-27.881726083278689},{"text":"MAE","x":0,"y":0.5,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(0,0,0,1)","family":"","size":14.611872146118724},"xref":"paper","yref":"paper","textangle":-90,"xanchor":"right","yanchor":"center","annotationType":"axis","xshift":-33.607305936073054},{"text":"invitro1","x":0.5,"y":1,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(255,255,255,1)","family":"","size":11.68949771689498},"xref":"paper","yref":"paper","textangle":-0,"xanchor":"center","yanchor":"bottom"}],"yaxis":{"domain":[0,1],"automargin":true,"type":"linear","autorange":false,"range":[0.082923247463492653,0.18292324746349264],"tickmode":"array","ticktext":["0.10","0.12","0.14","0.16","0.18"],"tickvals":[0.10000000000000001,0.12,0.14000000000000001,0.16,0.17999999999999999],"categoryorder":"array","categoryarray":["0.10","0.12","0.14","0.16","0.18"],"nticks":null,"ticks":"outside","tickcolor":"rgba(179,179,179,1)","ticklen":3.6529680365296811,"tickwidth":0.33208800332088001,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.68949771689498},"tickangle":-0,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"gridcolor":"rgba(222,222,222,1)","gridwidth":0.33208800332088001,"zeroline":false,"anchor":"x","title":"","hoverformat":".2f"},"shapes":[{"type":"rect","fillcolor":"transparent","line":{"color":"rgba(0,0,0,1)","width":0.66417600664176002,"linetype":"solid"},"yref":"paper","xref":"paper","x0":0,"x1":1,"y0":0,"y1":1},{"type":"rect","fillcolor":"rgba(179,179,179,1)","line":{"color":"transparent","width":0.66417600664176002,"linetype":"solid"},"yref":"paper","xref":"paper","x0":0,"x1":1,"y0":0,"y1":23.37899543378996,"yanchor":1,"ysizemode":"pixel"}],"showlegend":true,"legend":{"bgcolor":"rgba(255,255,255,1)","bordercolor":"transparent","borderwidth":1.8897637795275593,"font":{"color":"rgba(0,0,0,1)","family":"","size":11.68949771689498},"title":{"text":"Late integration","font":{"color":"rgba(0,0,0,1)","family":"","size":14.611872146118724}},"orientation":"h"},"hovermode":"closest","barmode":"relative"},"config":{"doubleClick":"reset","modeBarButtonsToAdd":["hoverclosest","hovercompare"],"showSendToCloud":false},"source":"A","attrs":{"3ce2dd686d7387":{"x":{},"y":{},"fill":{},"type":"box"}},"cur_data":"3ce2dd686d7387","visdat":{"3ce2dd686d7387":["function (y) ","x"]},"highlight":{"on":"plotly_click","persistent":false,"dynamic":false,"selectize":false,"opacityDim":0.20000000000000001,"selected":{"opacity":1},"debounce":0},"shinyEvents":["plotly_hover","plotly_click","plotly_selected","plotly_relayout","plotly_brushed","plotly_brushing","plotly_clickannotation","plotly_doubleclick","plotly_deselect","plotly_afterplot","plotly_sunburstclick"],"base_url":"https://plot.ly"},"evals":[],"jsHooks":[]}</script>

3.3 RMSE

3.3.1 PP

3.3.2 FS

3.3.3 LI

3.4 Spearman correlation (row)

3.4.1 PP

3.4.2 FS

3.4.3 LI

3.5 Aitchison distance

3.5.1 PP

3.5.2 FS

3.5.3 LI